SPINE: Bridging the Cyber-Physical Gap with Agentic AI
Quick Answer
SPINE is a novel framework that enhances the deployment of bimanual robots by enabling non-experts to debug and calibrate them effectively.
Quick Take
In trials, novices using SPINE achieved a 100% operationalization success rate and reduced teleoperation time significantly, outperforming expert operators. This advancement could bridge the gap in scalable applications.
Key Points
- SPINE integrates a profile builder and a debugger for efficient robot deployment.
- Novices improved operational success from 75% to 100% using SPINE in trials.
- Teleoperation time reduced from 16:45 to 13:47 minutes with SPINE.
- SPINE resolved all 10 bugs on AgileX PiPER, outperforming expert baseline.
- The framework reduces reliance on expert calibration for bimanual robots.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Foundation models have given robots a sophisticated brain for complex decision-making, yet deploying that intelligence into a physical platform still demands tedious, expert-driven calibration. This deployment gap, the robot's spinal cord, remains a primary bottleneck to scalable Embodied AI. Hence, we propose SPINE (Scalable Physical Integration with ageNtic Expertise): an agentic framework for systematically debugging and deploying bimanual robots with minimal robotics expertise. SPINE's harness comprises two orchestrated multi-agent workflows: a profile builder that creates robot-specific context, and a debugger that cycles through diagnosis, repair, and validation until teleoperation works. Across seven DOBOT X-Trainer debugging scenarios, a robotics novice using SPINE outperformed human operators using Claude Code with the same reference materials, but without SPINE's structured workflow, improving operationalization success from 75% to 100% and reducing mean time-to-teleoperation from 16 min 45 s to 13 min 47 s. On AgileX PiPER, a distinct ROS/CAN bimanual arm, SPINE resolved all 10 implanted bugs, versus 9 out of 10 for the expert baseline, in nearly the same amount of time. Together, these results show that SPINE can transfer across bimanual platforms, reduce dependence on expert calibration, and move embodied AI closer to scalable real-world deployment.
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2607.13049 [cs.AI] |
| (or arXiv:2607.13049v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.13049 arXiv-issued DOI via DataCite |
Submission history
From: Chan Lee [view email]
[v1]
Mon, 29 Jun 2026 20:09:51 UTC (3,436 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.